We and others have recently obtained the result that it is possible to
automatically infer a quantitative model of an organisms metabolic
network from the genome sequence of that organism.

Flux Balance Analysis (FBA) can be applied to metabolic models to
predict the growth rate of an organism, analyze the effect of gene
deletions, and more. But obtaining a working FBA model can be
challenging and time consuming. There are numerous reasons that a
model may not provide appropriate results or no result at all (i.e.,
an infeasible model). Indeed, a workable FBA model is based on: 1) A
sufficiently rich set of balanced reactions; 2) a correct set of
biomass metabolites; 3) an appropriate set of secreted metabolites;
and 4) a sufficient set of nutrient compounds. If only one of these
requirements is not met, or even a single critical reaction is
missing, flux-balance analyses cannot be performed.

We have recently developed methods for generating FBA models from
metabolic databases, and for guiding the user in correcting certain
classes of infeasible FBA models. Together these methods greatly
reduce the time required to obtain a working FBA model.

This software tool is based on Mixed Integer Linear Programming. It
obtains a working FBA model using a multiple gap-filling
approach. Starting from a possibly incomplete set of reactions,
nutrients, secretions, and biomass metabolites, multiple gap-filling
completes these sets to obtain a feasible FBA model by adding new
reactions from a reaction database and new secretions, nutrients, and
biomass metabolites from user provided try-sets.

In a typical scenario, a user provides a base set of reactions for the
organism, and try-sets for the biomass, secretions, and nutrients. The
tool adds as many metabolites as possible from the biomass try-set
using a minimum number of added reactions, nutrients, and secretions
of the try-sets to get a workable FBA model. Therefore, the method
identifies new reactions to add to the model, it identifies minimal
sets of required nutrients, and it identifies the maximal set of
biomass components that can be produced by the completed
model. Various parameters are provided to meet other scenarios.

Bio for Mario Latendresse and Peter Karp

Note for Visitors to SRI

Please arrive at least 10 minutes early as you will need to sign in by
following instructions by the lobby phone at Building E. SRI is located
at 333 Ravenswood Avenue in Menlo Park. Visitors may park in the parking
lots off Fourth Street. Detailed directions to SRI, as well as maps, are
available from the Visiting AIC web page.
There are two entrances to SRI International located on Ravenswood Ave.
Please check the Builing E entrance signage.